In order to graduate with the Quantitative Methods qualifier through one of our affiliated schools, you will need to take 20 credits of Q-Step modules in years one, two and three of your degree, as well as applying quantitative methods to their dissertation.
I enjoyed maths at A level and saw Q-Step as an opportunity to continue using it as part of my degree programme. The placement opportunity was mentioned when the programme was presented to us and this incentivised me further.
Benedict Watling, geography student
Quantitative Methods for Social Science 1
This module focuses on quantitative methods for the social sciences and in particular the acquisition of quantitative literacy.
The main topics of interest are:
- the character and nature of quantitative data
- their visualisation
- their use in descriptive statistics
- inference and estimation from random samples
- hypothesis testing
- working with statistical software
In-seminar activities allow you to work in small groups to explore first-hand concepts such as sampling, level of measurement and sampling variability.
Quantitative Methods for Social Science 2
Continuing from Quantitative Methods for Social Science 1, this module covers bivariate relationships and multiple regression, and their application in empirical social science research. Practical training such as use of these methods in empirical social science is key.
Intermediate Quantitative Methods for Social Science
Focused on multivariate regression analysis based on the concept of generalised linear models, this module covers linear, logistic and Poisson regression.
It emphasises the underlying similarity of these methods, the choice of the right method for specific problems, common aspects of model construction, the testing of model assumptions through influence and residual analyses, and the use of graphical and other methods to present results.
In addition to covering generalised linear models, you explore the use of multivariate regression analysis with large and complex data sets, including multi-level and longitudinal data. Information on best practices for data collection, data analysis and replication are integrated into lecture and seminar content.
Designing and Constructing Quantitative Social Research
Introducing you to the collection and analysis of social research data, this module has a particular focus on understanding the contexts in which different research strategies are appropriate. It will equip you with a critical understanding of the strengths and weaknesses of different research methodologies.
Workshop activities and assessment provide you with opportunities to put the principles into practice. You will gain insight and experience in the use of various research methods in order to strengthen your approach to your year-three dissertation.
Advanced Quantitative Methods for Social Science
In the social sciences, there is an increasing need to analyse situations where observations are grouped, such as individuals nested within geographical areas or organisations, and repeated observations of individuals over time in a panel survey. Multilevel modelling is a popular method that allows for the analysis of these clustered data.
This module will extend upon generalised linear modelling techniques (covered in intermediate quantitative methods), starting with the basic theory of multilevel models including random intercept and random slope specifications, the use of contextual variables in multilevel analysis, and modelling repeated measures. The module will focus on the practical application of multilevel models for continuous and binary outcomes using multilevel linear and logistic regression.
You will get hands-on training to carry out multilevel analyse and generate compelling data visualisations to communicate complex social patterns.